Abstract: Swarm robotics is a promising approach for the coordination of large numbers
of robots. While previous studies have shown that evolutionary robotics
techniques can be applied to obtain robust and efficient self-organized
behaviors for robot swarms, most studies have been conducted in simulation, and
the few that have been conducted on real robots have been confined to
laboratory environments. In this paper, we demonstrate for the first time a
swarm robotics system with evolved control successfully operating in a real and
uncontrolled environment. We evolve neural network-based controllers in
simulation for canonical swarm robotics tasks, namely homing, dispersion,
clustering, and monitoring. We then assess the performance of the controllers
on a real swarm of up to ten aquatic surface robots. Our results show that the
evolved controllers transfer successfully to real robots and achieve a
performance similar to the performance obtained in simulation. We validate that
the evolved controllers display key properties of swarm intelligence-based
control, namely scalability, flexibility, and robustness on the real swarm. We
conclude with a proof-of-concept experiment in which the swarm performs a
complete environmental monitoring task by combining multiple evolved
controllers.